Filter Debris Analysis for Aircraft Engine and Gearbox Health Management

Proceedings of the Institution of Mechanical Engineers, Part J:

Abanteriba

Dutton

et al. 2015

Spectrometric oil analysis has been a popular technique used by oil analysis laboratories to determine the concentration of elements in an oil sample. The primary application of this technique has been the identification of fine wear in lubricated systems as an indicator of abnormal wear in order to prevent failure. It has also found some use for monitoring contaminants and fluid additives. While the literature has varying accounts of the effectiveness of this technique for machinery condition monitoring purposes, the introduction of fine filtration to many modern machines has profound implications for the effectiveness of spectrometric oil analysis. This paper will assess the effectiveness of spectrometric oil analysis in a fine filtered system and compare it with the results from an inductive wear debris sensor. The inductive wear debris sensor is a relatively new sensor for detecting abnormal wear in recirculating lubrication systems. A combination of experimental data and aircraft data is presented.

Section: Inductive Wear Debris Sensors (Iwdss)mentioning

confidence: 99%

On the impact of fine filtration on spectrometric oil analysis and inductive wear debris sensors

Proceedings of the Institution of Mechanical Engineers, Part J:

Abanteriba

Dutton

et al. 2015

“…where C D and R e are the drag coefficient and Reynolds number, respectively, ; is the sphericity of the non-spherical particle. Reynolds number and sphericity of a settling particle can be calculated from the following equations (3) and (4). Where S sphere is the surface area of a sphere of the same volume as the particle; S particle is the actual surface area of the particle.…”

Section: Mathematical Analysismentioning

confidence: 99%

“…Metallic wear debris is one of the main causes of failures in power transmission systems. [1][2][3][4] Wear debris is defined as tiny metallic particles liberated from loadbearing surfaces into the lubricant mainly due to the following three different wear mechanisms: abrasive, rolling contact fatigue and adhesive wear. 5 In the past decades, many filtration systems such as magnetic filters and spin-on filters have been applied to gearboxes.…”

Section: Introductionmentioning

confidence: 99%

A novel passive filtration system for splash-lubricated gearboxes: Part 1

Yang

Abanteriba

Proceedings of the Institution of Mechanical Engineers, Part J:

2020

Splash-lubricated gearboxes are widely used in many industries. Contaminant removal systems are rarely employed in splash-lubricated gearboxes, such as some gearboxes are used in helicopters and mining machines. The maintenance of lubrication systems in those machines relies on periodic onsite inspection and lubricant change. However, wear debris generated during operation will suspend in lubricant and cause damage to contacting surfaces of gearbox’s components. In this first part of a two-part paper, a novel light-weight passive filtration system that utilises gravitational sedimentation and shape memory alloys is presented for unfiltered splash-lubricated gearboxes. A predictive model is developed for the estimation of flake-like wear particles’ settling behavior in the lubricant. Experiments based on a simplified test rig are also conducted to validate the theoretical results. This mathematical model can also be used to elucidate the impact of sedimentation time of wear particles on the cleanliness of the lubricant after the installation of passive filtration system.

“…Typically an Inductive Wear Debris Sensor (IWDS) is installed in the scavenge line of a recirculating lubrication system to detect incipient machinery failure by sensing metallic debris in the lubricant and has been applied to areas such as aviation, wind turbines, marine propulsion and general industry. [1][2][3] An IWDS detects the passage of metallic particles through an induced magnetic field that surrounds the bore of the sensor. The disruption to the magnetic field caused by the passage of the metallic particle results in a signal that is detected and then processed to provide information about type of particle (i.e.…”

Section: Introductionmentioning

confidence: 99%

Health indicator metrics applicable to inductive wear debris sensors

Proceedings of the Institution of Mechanical Engineers, Part J:

2016

The Inductive Wear Debris Sensor is a relatively new invention that is increasingly being used for the detection of incipient machinery damage or failures by sensing metallic debris in lubrication systems. This type of sensor is typically used in-line and has a superior particle size detection range compared to traditional techniques such as the ubiquitous spectrometric oil analysis. There is, however, very little in the literature regarding the application and interpretation of data arising from this type of sensor. Unlike other condition monitoring sensors, no data will be generated by an Inductive Wear Debris Sensor in an ideal system; however, in real applications it is necessary to discriminate between occasional particles unrelated to a failure and incipient failure particles. Inductive Wear Debris Sensor data could be misinterpreted if a simple cumulative count limit was applied to the data. A short-term rate of particle generation is sometimes used as an alternative; however, it too can be misleading with short succession particles producing high instantaneous rates possibly causing false alarms. The purpose of this work was to develop a robust metric (or group of metrics) that when applied to Inductive Wear Debris Sensor data would reliably identify a failure event and exclude non-failure related particles. The Health Indicator described herein consists of three subordinate Condition Indices that collectively are shown to reliably detect the onset of rolling contact fatigue. The metrics have been applied to bearing test rig data (seeded fault) and data obtained from a non-seeded fault test of a complex helicopter gearbox.